BaylorEdPsych (version 0.5)

PseudoR2: Pseudo-R2 Statistics

Description

Returns various pseudo-$R^2$ statistics (as well as Akaike's [corrected] information criterion) from a glm object. Should mimic those returend from a logistic/probit regression in Stata when using fitstat

Usage

PseudoR2(glmModel)

Arguments

glmModel

Object from a glm model, preferably logsitstic regression, e.g., family=binomial(link="logit")

Value

McFadden

McFadden Pseudo-$R^2$

Adj.McFadden

McFadden Adjusted Pseudo-$R^2$

Cox.Snell

Cox and Snell Pseudo-$R^2$ (also known as ML Pseudo-$R^2$)

Nagelkerke

Nagelkerke Pseudo$R^2$

McKelvey.Zavoina

McKelvey and Zavoina Pseudo-$R^2$

Effron

Effron Pseudo-$R^2$

Count

Count Pseudo-$R^2$, number of correctly classified cases, uisng $\hat{\pi}> .50$ as the cutoff

Adj.Count

Adjusted Count Pseudo-$R^2$

AIC

Akaike's information criterion

Corrected.AIC

Corrected Akaike information criterion

Details

None

References

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716--723. Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.). New York: Springer-Verlag.

Efron, B. (1978). Regression and ANOVA with zero-one data: Measures of residual variation. Journal of the American Statistical Association, 73(361), 113--121.

Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). Hoboke, NJ: Wiley.

Long, J. S.(1997). Regression models for categorical and limited dependent variables. Thousand Oaks, CA:Sage.

McFadden, D. (1979). Quantitative methods for analysing travel behavior of individuals: Some recent developments. In D. A. Hensher & P. R. Stopher (Eds.), Behavioural travel modelling (pp. 279--318). London: Croom Helm.

McKelvey, R. D., & Zavoina, W. (1975). A statistical model for the analysis of ordinal level dependent variables. The Journal of Mathematical Sociology, 4(1), 103--120

Nagelkerke, N. J. D. (1991). A note on a general definition of the coefficient of determination. Biometrika, 78(3), 691--692.

See Also

glm

Examples

data(MLBOffense2011)
MLBOffense2011$NL<-ifelse(MLBOffense2011$Lg=="NL", 1,0)
#predict MLB league membership from RBI and slugging
model1<-glm(NL~RBI + SLG, data=MLBOffense2011, family=binomial(link="logit"))  
PseudoR2(model1)